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Volume 25
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Sensors, Volume 25, Issue 6 (March-2 2025) – 316 articles

Cover Story (view full-size image): In response to the accurate positioning issues related to high-speed moving lens groups in rapid zoom optical lenses with voice coil motors (VCMs), we demonstrate a positioning system design based on tunnel magnetoresistance sensors. Based on analytical computation, the optimal air gap between the sensor and the magnetic grating is determined. We quantify the magnetic interference of the VCM using three-dimensional flux leakage mapping, deriving an optimal sensor position. Position error caused by interference remains below 5 μm with maximum deviations at the trajectory endpoints of the moving group. Our study provides a comprehensive framework for the design and optimization of magnetic positioning systems in optical applications with electromagnetic motors. View this paper
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30 pages, 6268 KiB  
Article
Cooperative Hybrid Modelling and Dimensionality Reduction for a Failure Monitoring Application in Industrial Systems
by Morgane Suhas, Emmanuelle Abisset-Chavanne and Pierre-André Rey
Sensors 2025, 25(6), 1952; https://doi.org/10.3390/s25061952 - 20 Mar 2025
Cited by 1 | Viewed by 261
Abstract
Failure monitoring of industrial systems is imperative in order to ensure their reliability and competitiveness. This paper presents an innovative hybrid modelling approach applied to DC electric motors, specifically the Kollmorgen AKM42 servomotor. The proposed Cooperative Hybrid Model for Classification (CHMC) combines physics-based [...] Read more.
Failure monitoring of industrial systems is imperative in order to ensure their reliability and competitiveness. This paper presents an innovative hybrid modelling approach applied to DC electric motors, specifically the Kollmorgen AKM42 servomotor. The proposed Cooperative Hybrid Model for Classification (CHMC) combines physics-based and data-driven models to improve fault detection and extrapolation to new usage profiles. The integration of physical knowledge of the healthy behaviour of the motor into a recurrent neural network enhances the accuracy of bearing fault detection by identifying three health states: healthy, progressive fault and stabilised fault. Additionally, Singular Value Decomposition (SVD) is employed for the purposes of feature extraction and dimensionality reduction, thereby enhancing the model’s capacity to generalise with limited training data. The findings of this study demonstrate that a reduction in the input data of 90% preserves the essential information, with an analysis of the first harmonics revealing a narrow frequency range. This elucidates the reason why the first 20 components are sufficient to explain the data variability. The findings reveal that, for usage profiles analogous to the training data, both the CHMC and NHMC models demonstrate comparable performance without reduction. However, the CHMC model exhibits superior performance in detecting true negatives (90% vs. 89%) and differentiating between healthy and failure states. The NHMC model encounters greater difficulty in distinguishing failure states (83.92% vs. 86.56% for progressive failure). When exposed to new usage profiles with increased frequency and amplitude, the CHMC model adapts better, showing superior performance in detecting true positives and handling new data, highlighting its superior extrapolation capabilities. The integration of SVD further reduces input data complexity, and the CHMC model consistently outperforms the NHMC model in these reduced data scenarios, demonstrating the efficacy of combining physical models and dimensionality reduction in enhancing the model’s generalisation, fault detection, and adaptability. This approach has the advantage of reducing the need for retraining, which makes the CHMC model a cost-effective solution for motor fault classification in industrial settings. In conclusion, the CHMC model offers a generalisable method with significant advantages in fault detection, model adaptation, and predictive maintenance performance across varying usage profiles and on unseen operational scenarios. Full article
(This article belongs to the Section Industrial Sensors)
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21 pages, 11171 KiB  
Article
A Beam Steering Vector Tracking GNSS Software-Defined Receiver for Robust Positioning
by Scott Burchfield, Charles Givhan and Scott Martin
Sensors 2025, 25(6), 1951; https://doi.org/10.3390/s25061951 - 20 Mar 2025
Viewed by 219
Abstract
Global navigation satellite systems are the best means of navigation for dynamic platforms. However, interference, line-of-sight blockages, and multipath are destructive to receiver operations. Advanced receiver architectures like vector tracking loops have been shown to be more resilient in tracking during degraded signal [...] Read more.
Global navigation satellite systems are the best means of navigation for dynamic platforms. However, interference, line-of-sight blockages, and multipath are destructive to receiver operations. Advanced receiver architectures like vector tracking loops have been shown to be more resilient in tracking during degraded signal environments and dynamic scenarios. Additionally, controlled reception pattern antennas can be used to steer the effective antenna gain pattern to resist interference. This work introduces algorithms for a software-defined radio that combines vector tracking loops with a phased antenna array to digitally steer beams for the amplification of signals of interest so that the effects of signal degradation and multipath can be reduced. The proposed receiver design was tested on dynamic live sky data in multipath-rich environments and compared against traditional scalar receivers with and without beamforming as well as robust commercial receivers. The results showed that beam steering receivers were obtaining the expected amplification and that the vector tracking with beam steering was able to provide better positioning and signal tracking performance than the other implemented software receivers and provide continuous measurements where the commercial receiver failed to track degraded signals. Full article
(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)
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17 pages, 17410 KiB  
Article
Remaining Useful Life Prediction Method for Bearings Based on Pruned Exact Linear Time State Segmentation and Time–Frequency Diagram
by Xu Wei, Jingjing Fan, Huahua Wang and Lulu Cai
Sensors 2025, 25(6), 1950; https://doi.org/10.3390/s25061950 - 20 Mar 2025
Viewed by 179
Abstract
To improve the accuracy and robustness of bearing remaining useful life (RUL) prediction, this paper proposes a bearing RUL prediction method based on PELT state segmentation and time–frequency analysis, incorporating the Informer model for time-series modeling. First, the PELT (Pruned Exact Linear Time) [...] Read more.
To improve the accuracy and robustness of bearing remaining useful life (RUL) prediction, this paper proposes a bearing RUL prediction method based on PELT state segmentation and time–frequency analysis, incorporating the Informer model for time-series modeling. First, the PELT (Pruned Exact Linear Time) algorithm is used to segment the vibration signals over the full life cycle of the bearing, accurately identifying critical degradation states and optimizing the stage division of the degradation process. Next, wavelet transform is applied to perform time–frequency analysis on the vibration signals, generating time–frequency spectrograms to comprehensively extract features in both the time and frequency domains. Finally, the extracted time–frequency features are used as input to predict the bearing RUL using the Informer model. As an efficient time-series prediction model, the Informer excels at handling long time series by leveraging a sparse self-attention mechanism to effectively capture the long-term dependencies in the signals. Experiments conducted on a publicly available dataset and comparisons with traditional methods demonstrate that the proposed method offers significant advantages in terms of prediction accuracy, computational efficiency, and robustness, making it more suitable for bearing health assessment and RUL prediction under complex working conditions. Full article
(This article belongs to the Section Physical Sensors)
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25 pages, 5384 KiB  
Article
Prediction of Input–Output Characteristic Curves of Hydraulic Cylinders Based on Three-Layer BP Neural Network
by Wei Cai, Yirui Zhang, Jianxin Zhang, Shunshun Guo and Rui Guo
Sensors 2025, 25(6), 1949; https://doi.org/10.3390/s25061949 - 20 Mar 2025
Viewed by 182
Abstract
To predict the variation in the displacement position of hydraulic cylinder piston rods, a neural network model is proposed to enhance the displacement control accuracy of hydraulic cylinders. The innovation of this paper is that by calculating the compressibility-induced flow loss of hydraulic [...] Read more.
To predict the variation in the displacement position of hydraulic cylinder piston rods, a neural network model is proposed to enhance the displacement control accuracy of hydraulic cylinders. The innovation of this paper is that by calculating the compressibility-induced flow loss of hydraulic fluid, mathematical models for both the internal and external leakage of hydraulic cylinders are established, identifying seven primary factors influencing piston rod displacement. Because there are many influencing factors and complex parameters different from traditional backpropagation (BP) neural network used in previous studies, this paper innovatively proposes a three-layer BP neural network ensemble model for predicting input–output characteristic curves of hydraulic cylinders. In the process of model improvement, a nonlinear adaptive decreasing weight mechanism is introduced to improve the optimization accuracy of the algorithm, facilitating the search for optimal solutions. The most reasonable weight and bias parameters are determined via the iterative training and testing of each BP neural network layer. This model enables the real-time prediction of piston rod displacement output curves after a specified time interval based on external input parameters. The predicted time is utilized to compensate for the response delays caused by directional valve switching and hydraulic fluid buffering, thereby enabling proactive displacement prediction. Validation results demonstrate that the maximum predicted displacement error is reduced to 0.5491 µm, with the model’s maximum runtime being 27.82 ms. The maximum allowable time allocated for directional valve switching and fluid buffering in the hydraulic system is extended to 74.57 ms, achieving the objective of enhancing both displacement control accuracy and operational efficiency. Full article
(This article belongs to the Section Physical Sensors)
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18 pages, 5999 KiB  
Article
Simulation and Modelling of C+L+S Multiband Optical Transmission for the OCATA Time Domain Digital Twin
by Prasunika Khare, Nelson Costa, Marc Ruiz, Antonio Napoli, Jaume Comellas, Joao Pedro and Luis Velasco
Sensors 2025, 25(6), 1948; https://doi.org/10.3390/s25061948 - 20 Mar 2025
Viewed by 165
Abstract
C+L+S multiband (MB) optical transmission has the potential to increase the capacity of optical transport networks, and thus, it is a possible solution to cope with the traffic increase expected in the years to come. However, the introduction of MB optical technology needs [...] Read more.
C+L+S multiband (MB) optical transmission has the potential to increase the capacity of optical transport networks, and thus, it is a possible solution to cope with the traffic increase expected in the years to come. However, the introduction of MB optical technology needs to come together with the needed tools that support network planning and operation. In particular, quality of transmission (QoT) estimation is needed for provisioning optical MB connections. In this paper, we concentrate on modelling MB optical transmission for provide fast and accurate QoT estimation and propose machine learning (ML) approaches based on neural networks, which can be easily integrated into an optical layer digital twin (DT) solution. We start by considering approaches that can be used for accurate signal propagation modelling. Even though solutions such as the split-step Fourier method (SSFM) for solving the nonlinear Schrödinger equation (NLSE) have limited application for QoT estimation during provisioning because of their very high complexity and time consumption, they could be used to generate datasets for ML model creation. However, even that can be hard to carry out on a fully loaded MB system with hundreds of channels. In addition, in MB optical transmission, interchannel stimulated Raman scattering (ISRS) becomes a major effect, which adds more complexity. In view of that, the fourth-order Runge–Kutta in the interaction picture (RK4IP) method, complemented with an adaptive step size algorithm to further reduce the computation time, is evaluated as an alternative to reduce time complexity. We show that RK4IP provided an accuracy comparable to that of the SSFM with reduced computation time, which enables its application for MB optical transmission simulation. Once datasets were generated using the adaptive step size RK4IP method, two ML modelling approaches were considered to be integrated in the OCATA DT, where models predict optical signal propagation in the time domain. Being able to predict the optical signal in the time domain, as it will be received after propagation, opens opportunities for automating network operation, including connection provisioning and failure management. In this paper, we focus on comparing the proposed ML modelling approaches in terms of the models’ general and QoT estimation accuracy. Full article
(This article belongs to the Section Communications)
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10 pages, 3108 KiB  
Article
Non-Invasive Wide-Field Imaging of Chip Surface Temperature Distribution Based on Ensemble Diamond Nitrogen-Vacancy Centers
by Zhenrong Shi, Ziwen Pan, Qinghua Li and Wei Li
Sensors 2025, 25(6), 1947; https://doi.org/10.3390/s25061947 - 20 Mar 2025
Viewed by 203
Abstract
With the development of chip technology, the demand for device reliability in various electronic chip industries continues to grow. In recent years, with the advancement of quantum sensors, the solid-state spin (nitrogen-vacancy) NV center temperature measurement system has garnered attention due to its [...] Read more.
With the development of chip technology, the demand for device reliability in various electronic chip industries continues to grow. In recent years, with the advancement of quantum sensors, the solid-state spin (nitrogen-vacancy) NV center temperature measurement system has garnered attention due to its high sensitivity and spatial range. However, NV centers are not only affected by temperature but also by magnetic fields. This article analyzes the impact of magnetic fields on temperature detection. By combining the wide-field imaging platform of optically detected magnetic resonance (ODMR) with a temperature-sensitive structure of thin ensemble diamond overlaid on a quartz substrate, high-sensitivity temperature detection has been achieved. And obtains a sensitivity of approximately 10 mK/Hz1/2. By combining a CCD camera imaging system, it realizes a wide field of view of 500 μm2, a high spatial resolution of 1.3 μm. Ultimately, this study demonstrates the two-dimensional actual temperature distribution on the chip surface under different currents, achieving wide-field, non-contact, high-speed temperature imaging of the chip surface. Full article
(This article belongs to the Special Issue Research Progress in Optical Microcavity-Based Sensing)
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18 pages, 7560 KiB  
Article
Research on Yield Prediction Model Driven by Mechanism and Data Fusion
by Xin Meng, Xingyu Liu, Hancong Duan, Ze Hu and Min Wang
Sensors 2025, 25(6), 1946; https://doi.org/10.3390/s25061946 - 20 Mar 2025
Viewed by 138
Abstract
Existing production forecasting methods often suffer from limited predictive accuracy due to their reliance on single-source data and the insufficient incorporation of physical principles. To address these challenges, this study proposes a mechanism–data fusion production forecasting model that integrates mechanistic model outputs with [...] Read more.
Existing production forecasting methods often suffer from limited predictive accuracy due to their reliance on single-source data and the insufficient incorporation of physical principles. To address these challenges, this study proposes a mechanism–data fusion production forecasting model that integrates mechanistic model outputs with data-driven learning techniques. The proposed method first establishes a three-phase-separator mechanistic model to generate physics-informed simulation data. Then, a Global–Local Branch Prediction Model is designed to enhance both long-term trend estimation and local feature capture in a production time series. The mechanistic model data are incorporated as constraints into the prediction framework, effectively guiding the learning process and improving forecast accuracy. Experimental results on real-world oilfield data demonstrate that the proposed model outperforms state-of-the-art methods such as Autoformer and DLinear. Specifically, under the mechanism-based approach, the Global–Local Branching Prediction Model reduces MSE by 0.0100, MAE by 0.0501, and RSE by 1.40% compared to Autoformer and achieves improvements of 0.0080 in MSE, 0.0093 in MAE, and 0.48% in RSE over DLinear. The results confirm that integrating mechanistic constraints significantly enhances prediction performance, making the proposed model a robust and technologically superior solution for production forecasting in petroleum engineering. Full article
(This article belongs to the Section Physical Sensors)
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16 pages, 2968 KiB  
Article
Combining 24-Hour Continuous Monitoring of Time-Locked Heart Rate, Physical Activity and Gait in Older Adults: Preliminary Findings
by Eitan E. Asher, Eran Gazit, Nasim Montazeri, Elisa Mejía-Mejía, Rachel Godfrey, David A. Bennett, Veronique G. VanderHorst, Aron S. Buchman, Andrew S. P. Lim and Jeffrey M. Hausdorff
Sensors 2025, 25(6), 1945; https://doi.org/10.3390/s25061945 - 20 Mar 2025
Viewed by 254
Abstract
Hemodynamic homeostasis is essential for adapting the heart rate (HR) to postural and physiological changes during daily activities. Traditional HR monitoring, such as 24 hour (h) Holter monitoring, provides important information on homeostasis during daily living. However, this approach lacks concurrent activity recording, [...] Read more.
Hemodynamic homeostasis is essential for adapting the heart rate (HR) to postural and physiological changes during daily activities. Traditional HR monitoring, such as 24 hour (h) Holter monitoring, provides important information on homeostasis during daily living. However, this approach lacks concurrent activity recording, limiting insights into hemodynamic adaptation and our ability to interpret changes in HR. To address this, we utilized a novel wearable sensor system (ANNE@Sibel) to capture time-locked HR and daily activity (i.e., lying, sitting, standing, walking) data in 105 community-dwelling older adults. We developed custom tools to extract 24 h time-locked measurements and introduced a “heart rate response score” (HRRS), based on root Jensen–Shannon divergence, to quantify HR changes relative to activity. As expected, we found a progressive HR increase with more vigorous activities, though individual responses varied widely, highlighting heterogeneous HR adaptations. The HRRS (mean: 0.38 ± 0.14; min: −0.11; max: 0.74) summarized person-specific HR changes and was correlated with several clinical measures, including systolic blood pressure changes during postural transitions (r = 0.325, p = 0.003), orthostatic hypotension status, and calcium channel blocker medication use. These findings demonstrate the potential of unobtrusive sensors in remote phenotyping as a means of providing valuable physiological and behavioral data to enhance the quantitative description of aging phenotypes. This approach could enhance personalized medicine by informing targeted interventions based on hemodynamic adaptations during everyday activities. Full article
(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)
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20 pages, 3029 KiB  
Article
The Parameter-Optimized Recursive Sliding Variational Mode Decomposition Algorithm and Its Application in Sensor Signal Processing
by Yunyi Liu, Wenjun He, Tao Pan, Shuxian Qin, Zhaokai Ruan and Xiangcheng Li
Sensors 2025, 25(6), 1944; https://doi.org/10.3390/s25061944 - 20 Mar 2025
Viewed by 160
Abstract
In industrial polishing, the sensor on the polishing motor needs to extract accurate signals in real time. Due to the insufficient real-time performance of Variational Mode Decomposition (VMD) for signal extraction, some studies have proposed the Recursive Sliding Variational Mode Decomposition (RSVMD) algorithm [...] Read more.
In industrial polishing, the sensor on the polishing motor needs to extract accurate signals in real time. Due to the insufficient real-time performance of Variational Mode Decomposition (VMD) for signal extraction, some studies have proposed the Recursive Sliding Variational Mode Decomposition (RSVMD) algorithm to address this limitation. However, RSVMD can exhibit unstable performance in strong-interference scenarios. To suppress this phenomenon, a Parameter-Optimized Recursive Sliding Variational Mode Decomposition (PO-RSVMD) algorithm is proposed. The PO-RSVMD algorithm optimizes RSVMD in the following two ways: First, an iterative termination condition based on modal component error mutation judgment is introduced to prevent over-decomposition. Second, a rate learning factor is introduced to automatically adjust the initial center frequency of the current window to reduce errors. Through simulation experiments with signals with different signal-to-noise ratios (SNR), it is found that as the SNR increases from 0 dB to 17 dB, the PO-RSVMD algorithm accelerates the iteration time by at least 53% compared to VMD and RSVMD; the number of iterations decreases by at least 57%; and the RMSE is reduced by 35% compared to the other two algorithms. Furthermore, when applying the PO-RSVMD algorithm and the RSVMD algorithm to the Inertial Measurement Unit (IMU) for measuring signal extraction performance under strong interference conditions after the polishing motor starts, the average iteration time and number of iterations of PO-RSVMD are significantly lower than those of RSVMD, demonstrating its capability for rapid signal extraction. Moreover, the average RMSE values of the two algorithms are very close, verifying the high real-time performance and stability of PO-RSVMD in practical applications. Full article
(This article belongs to the Section Industrial Sensors)
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23 pages, 1128 KiB  
Article
UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement Learning
by Junjie Yan, Yichen Xu, Haohao Yuan and Chunhua Xue
Sensors 2025, 25(6), 1943; https://doi.org/10.3390/s25061943 - 20 Mar 2025
Viewed by 191
Abstract
UAVs and reconfigurable intelligent surfaces (RISs) have emerged as promising solutions to enhance communication coverage and performance. However, existing studies primarily focus on optimizing the amplitude and phase shift of a STAR-RIS without considering the impact of varying UAV hovering angles on signal [...] Read more.
UAVs and reconfigurable intelligent surfaces (RISs) have emerged as promising solutions to enhance communication coverage and performance. However, existing studies primarily focus on optimizing the amplitude and phase shift of a STAR-RIS without considering the impact of varying UAV hovering angles on signal reflection and transmission. In this paper, we propose a novel STAR-RIS-assisted UAV service enhancement mechanism that dynamically adjusts reflection/transmission regions based on the real-time user distribution, significantly improving the channel quality for both edge and occluded users. This work is the first to jointly optimize the phase and amplitude of the STAR-RIS, the UAV flight trajectory, and the hovering angle, addressing the critical challenge of co-channel interference caused by dynamically partitioned service areas. The complex optimization problem is decomposed into subproblems, where the UAV flight trajectory is optimized using the Chained Lin–Kernighan (CLK) algorithm and the STAR-RIS parameters and UAV hovering angle are optimized using the TD3 algorithm. The experimental results show that the proposed mechanism effectively reduces the system service time and user transmission time, outperforming traditional methods. Full article
(This article belongs to the Section Communications)
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14 pages, 12439 KiB  
Article
An Efficient 3D Measurement Method for Shiny Surfaces Based on Fringe Projection Profilometry
by Hao Wei, Hongru Li, Xuan Li, Sha Wang, Guoliang Deng and Shouhuan Zhou
Sensors 2025, 25(6), 1942; https://doi.org/10.3390/s25061942 - 20 Mar 2025
Viewed by 340
Abstract
Fringe projection profilometry (FPP) is a widely employed technique owing to its rapid speed and high accuracy. However, when FPP is utilized to measure shiny surfaces, the fringes tend to be saturated or too dark, which significantly compromises the accuracy of the 3D [...] Read more.
Fringe projection profilometry (FPP) is a widely employed technique owing to its rapid speed and high accuracy. However, when FPP is utilized to measure shiny surfaces, the fringes tend to be saturated or too dark, which significantly compromises the accuracy of the 3D measurement. To overcome this challenge, this paper proposes an efficient method for the 3D measurement of shiny surfaces based on FPP. Firstly, polarizers are employed to alleviate fringe saturation by leveraging the polarization property of specular reflection. Although polarizers reduce fringe intensity, a deep learning method is utilized to enhance the quality of fringes, especially in low-contrast regions, thereby improving measurement accuracy. Furthermore, to accelerate measurement efficiency, a dual-frequency complementary decoding method is introduced, requiring only two auxiliary fringes for accurate fringe order determination, thereby achieving high-efficiency and high-dynamic-range 3D measurement. The effectiveness and feasibility of the proposed method are validated through a series of experimental results. Full article
(This article belongs to the Section Sensing and Imaging)
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25 pages, 3050 KiB  
Article
Optimizing Autonomous Vehicle Performance Using Improved Proximal Policy Optimization
by Mehmet Bilban and Onur İnan
Sensors 2025, 25(6), 1941; https://doi.org/10.3390/s25061941 - 20 Mar 2025
Viewed by 456
Abstract
Autonomous vehicles must make quick and accurate decisions to operate efficiently in complex and dynamic urban traffic environments, necessitating a reliable and stable learning mechanism. The proximal policy optimization (PPO) algorithm stands out among reinforcement learning (RL) methods for its consistent learning process, [...] Read more.
Autonomous vehicles must make quick and accurate decisions to operate efficiently in complex and dynamic urban traffic environments, necessitating a reliable and stable learning mechanism. The proximal policy optimization (PPO) algorithm stands out among reinforcement learning (RL) methods for its consistent learning process, ensuring stable decisions under varying conditions while avoiding abrupt deviations during execution. However, the PPO algorithm often becomes trapped in a limited search space during policy updates, restricting its adaptability to environmental changes and alternative strategy exploration. To overcome this limitation, we integrated Lévy flight’s chaotic and comprehensive exploration capabilities into the PPO algorithm. Our method helped the algorithm explore larger solution spaces and reduce the risk of getting stuck in local minima. In this study, we collected real-time data such as speed, acceleration, traffic sign positions, vehicle locations, traffic light statuses, and distances to surrounding objects from the CARLA simulator, processed via Apache Kafka. These data were analyzed by both the standard PPO and our novel Lévy flight-enhanced PPO (LFPPO) algorithm. While the PPO algorithm offers consistency, its limited exploration hampers adaptability. The LFPPO algorithm overcomes this by combining Lévy flight’s chaotic exploration with Apache Kafka’s real-time data streaming, an advancement absent in state-of-the-art methods. Tested in CARLA, the LFPPO algorithm achieved a 99% success rate compared to the PPO algorithm’s 81%, demonstrating superior stability and rewards. These innovations enhance safety and RL exploration, with the LFPPO algorithm reducing collisions to 1% versus the PPO algorithm’s 19%, advancing autonomous driving beyond existing techniques. Full article
(This article belongs to the Topic Information Sensing Technology for Intelligent/Driverless Vehicle, 2nd Edition)
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34 pages, 2272 KiB  
Article
Intelligent Fault-Tolerant Control of Delta Robots: A Hybrid Optimization Approach for Enhanced Trajectory Tracking
by Carlos Domínguez and Claudio Urrea
Sensors 2025, 25(6), 1940; https://doi.org/10.3390/s25061940 - 20 Mar 2025
Viewed by 224
Abstract
The kinematic complexity and multi-actuator dependence of Delta-type manipulators render them vulnerable to performance degradation from faults. This study presents a novel approach to Active Fault-Tolerant Control (AFTC) for Delta-type parallel robots, integrating an advanced fault diagnosis system with a robust control strategy. [...] Read more.
The kinematic complexity and multi-actuator dependence of Delta-type manipulators render them vulnerable to performance degradation from faults. This study presents a novel approach to Active Fault-Tolerant Control (AFTC) for Delta-type parallel robots, integrating an advanced fault diagnosis system with a robust control strategy. In the first stage, a fault diagnosis system is developed, leveraging a hybrid feature extraction algorithm that combines Wavelet Scattering Networks (WSNs), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Meta-Learning (ML). This system effectively identifies and classifies faults affecting single actuators, sensors, and multiple components under real-time conditions. The proposed AFTC approach employs a hybrid optimization framework that integrates Genetic Algorithms and Gradient Descent to reconfigure a Type-2 fuzzy controller. Results show that the methodology achieves perfect fault diagnosis accuracy across four classifiers and enhances robot performance by reducing critical degradation to moderate levels under multiple faults. These findings validate the robustness and efficiency of the proposed fault-tolerant control strategy, highlighting its potential for enhancing trajectory tracking accuracy in complex robotic systems under adverse conditions. Full article
(This article belongs to the Special Issue Sensing for Automatic Control and Measurement System)
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20 pages, 6567 KiB  
Article
Generalized q-Method Relative Pose Estimation for UAVs with Onboard Sensor Measurements
by Kyl Stanfield, Ahmad Bani Younes and Mohammad Hayajneh
Sensors 2025, 25(6), 1939; https://doi.org/10.3390/s25061939 - 20 Mar 2025
Viewed by 186
Abstract
The q-method for pose estimation utilizes on-board measurement vectors of reference objects to calculate air vehicle position and orientation with respect to an Inertial frame. This new method solves for the quaternion eigenvalue solution of the optimal pose to minimize the error in [...] Read more.
The q-method for pose estimation utilizes on-board measurement vectors of reference objects to calculate air vehicle position and orientation with respect to an Inertial frame. This new method solves for the quaternion eigenvalue solution of the optimal pose to minimize the error in the derived system of equations. The generalized q-method extends Davenport’s q-method for satellite attitude estimation by incorporating inertial position into the relative model and eliminating assumptions throughout the derivation that require spacecraft applications. Thus, the pose estimation model is developed and implemented for UAV applications using an onboard camera to obtain measurements in a controlled environment. Combined with numerical methods, algorithm outputs for position and orientation are validated against truth data to prove accurate estimation despite sensor error. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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21 pages, 11079 KiB  
Article
Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n
by Lili Song, Haixin Deng, Jianfeng Han and Xiongwei Gao
Sensors 2025, 25(6), 1938; https://doi.org/10.3390/s25061938 - 20 Mar 2025
Viewed by 181
Abstract
The water surface environment is highly complex, and floating objects in aerial images often occupy a minimal proportion, leading to significantly reduced feature representation. These challenges pose substantial difficulties for current research on the detection and classification of water surface floating objects. To [...] Read more.
The water surface environment is highly complex, and floating objects in aerial images often occupy a minimal proportion, leading to significantly reduced feature representation. These challenges pose substantial difficulties for current research on the detection and classification of water surface floating objects. To address the aforementioned challenges, we proposed an improved YOLOv8-HSH algorithm based on YOLOv8n. The proposed algorithm introduces several key enhancements: (1) an enhanced HorBlock module to facilitate multi-gradient and multi-scale superposition, thereby intensifying critical floating object characteristics; (2) an optimized CBAM attention mechanism to mitigate background noise interference and substantially elevate detection accuracy; (3) the incorporation of a minor target recognition layer to augment the model’s capacity to discern floating objects of differing dimensions across various environments; and (4) the implementation of the WIoU loss function to enhance the model’s convergence rate and regression accuracy. Experimental results indicate that the proposed strategy yields a significant enhancement, with mAP50 and mAP50-95 increasing by 11.7% and 12.4%, respectively, while the miss rate decreases by 11%. The F1 score has increased by 11%, and the average accuracy for each category of floating objects has enhanced by a minimum of 5.6%. These improvements not only significantly enhanced the model’s detection accuracy and robustness in complex scenarios but also provided new solutions for research in aerial image processing and related environmental monitoring fields. Full article
(This article belongs to the Special Issue Deep Learning Methods for Aerial Imagery)
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22 pages, 23377 KiB  
Article
Long-Term Wavelength Stability of Large Type II FBG Arrays in Different Silica-Based Fibers at High Temperature
by Robert B. Walker, Stephen J. Mihailov, Cyril Hnatovsky, Manny De Silva, Ping Lu, Huimin Ding and Abdullah Rahnama
Sensors 2025, 25(6), 1937; https://doi.org/10.3390/s25061937 - 20 Mar 2025
Viewed by 223
Abstract
Fiber Bragg gratings (FBGs) are useful components in fiber optic sensing systems, which can be highly multiplexed and distributed. In recent years, fabrication using ultrafast lasers has made these devices much more versatile and robust, but questions concerning their high-temperature performance remain. The [...] Read more.
Fiber Bragg gratings (FBGs) are useful components in fiber optic sensing systems, which can be highly multiplexed and distributed. In recent years, fabrication using ultrafast lasers has made these devices much more versatile and robust, but questions concerning their high-temperature performance remain. The wavelength resonance of an FBG is naturally sensitive to various parameters of its environment; in particular, changes in the temperature or strain of a fiber tend to induce observable shifts in the Bragg wavelength. Thus, FBGs can offer reliable sensing solutions, provided they are isolated from other influences and their wavelength responses remain well characterized. Nonetheless, it is important to be aware that the isothermal wavelength drift of unstrained FBGs has been previously observed. When this occurs, it can lead to measurement errors and a requirement for sensor recalibration. This study presents a comparison of long-term isothermal wavelength drifts observed at 600 °C, 800 °C, 900 °C and 1000 °C for large numbers of Type II FBGs in different kinds of single-mode fibers. The results provide guidance for the design of high-temperature sensing systems, both in terms of fiber selection and for estimating the maximum time before recalibration becomes necessary to maintain a specified accuracy. Full article
(This article belongs to the Special Issue Distributed Fiber Optic Sensors for High Temperature Sensing Applications)
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42 pages, 14097 KiB  
Review
Microfluidic Biosensors: Enabling Advanced Disease Detection
by Siyue Wang, Xiaotian Guan and Shuqing Sun
Sensors 2025, 25(6), 1936; https://doi.org/10.3390/s25061936 - 20 Mar 2025
Viewed by 738
Abstract
Microfluidic biosensors integrate microfluidic and biosensing technologies to achieve the miniaturization, integration, and automation of disease diagnosis, and show great potential for application in the fields of cancer liquid biopsy, pathogenic bacteria detection, and POCT. This paper reviews the recent advances related to [...] Read more.
Microfluidic biosensors integrate microfluidic and biosensing technologies to achieve the miniaturization, integration, and automation of disease diagnosis, and show great potential for application in the fields of cancer liquid biopsy, pathogenic bacteria detection, and POCT. This paper reviews the recent advances related to microfluidic biosensors in the field of laboratory medicine, focusing on their applications in the above three areas. In cancer liquid biopsy, microfluidic biosensors facilitate the isolation, enrichment, and detection of tumor markers such as CTCs, ctDNA, miRNA, exosomes, and so on, providing support for early diagnosis, precise treatment, and prognostic assessment. In terms of pathogenic bacteria detection, microfluidic biosensors can achieve the rapid, highly sensitive, and highly specific detection of a variety of pathogenic bacteria, helping disease prevention and control as well as public health safety. Pertaining to the realm of POCT, microfluidic biosensors bring the convenient detection of a variety of diseases, such as tumors, infectious diseases, and chronic diseases, to primary health care. Future microfluidic biosensor research will focus on enhancing detection throughput, lowering costs, innovating new recognition elements and signal transduction methods, integrating artificial intelligence, and broadening applications to include home health care, drug discovery, food safety, and so on. Full article
(This article belongs to the Special Issue Recent Advances in Microfluidic Sensing Devices)
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22 pages, 15026 KiB  
Article
Localization of Radio Sources Using High Altitude Platform Station (HAPS)
by Yuta Furuse and Gia Khanh Tran
Sensors 2025, 25(6), 1935; https://doi.org/10.3390/s25061935 - 20 Mar 2025
Viewed by 195
Abstract
In Japan, the DEURAS system has been deployed to detect and locate illegal radio sources that either exceed permissible transmission power limits or operate on unauthorized frequencies. This system utilizes receiving antennas installed on high-rise buildings and radio towers to capture radio signals [...] Read more.
In Japan, the DEURAS system has been deployed to detect and locate illegal radio sources that either exceed permissible transmission power limits or operate on unauthorized frequencies. This system utilizes receiving antennas installed on high-rise buildings and radio towers to capture radio signals and estimate the location of the transmission source. However, in densely built urban environments, the accuracy of location estimation is often compromised due to signal reflections and diffractions. Additionally, in large-scale disasters such as earthquakes, terrestrial infrastructure may be severely damaged, making it essential to develop a localization system that operates independently of ground-based stations. To overcome these limitations, this study proposes a localization system based on a high-altitude-platform station (HAPS), which operates at an altitude of approximately 20 km. The feasibility and effectiveness of the proposed system are evaluated through numerical simulations, considering various environmental conditions. The results demonstrate that HAPS-based localization significantly improves positioning accuracy, offering a robust and high-precision alternative for radio source detection, particularly in scenarios where traditional ground-based systems are unreliable or unavailable. Full article
(This article belongs to the Section Navigation and Positioning)
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17 pages, 1096 KiB  
Article
Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings
by Pengyu Zhu, Youwei Li, Peidong Xu, Ping Li, Zhenbing Zhao and Gang Li
Sensors 2025, 25(6), 1934; https://doi.org/10.3390/s25061934 - 20 Mar 2025
Viewed by 181
Abstract
In the power industry, secondary operation risk assessment is a critical step in ensuring operational safety. However, traditional assessment methods often rely on expert judgment, making it difficult to efficiently address the challenges posed by unstructured textual data and complex equipment relationships. To [...] Read more.
In the power industry, secondary operation risk assessment is a critical step in ensuring operational safety. However, traditional assessment methods often rely on expert judgment, making it difficult to efficiently address the challenges posed by unstructured textual data and complex equipment relationships. To address this issue, this paper proposes a hybrid model that integrates graph convolutional networks (GCNs) with semantic embedding techniques. The model consists of two main components: the first constructs a domain-specific knowledge graph for the power industry and uses a GCN to extract structural information, while the second fine-tunes the RoBERTa pre-trained model to generate semantic embeddings for textual data. Finally, the model employs a hybrid similarity measurement mechanism that comprehensively considers both semantic and structural features, combining K-means clustering similarity search with a multi-node weighted evaluation method to achieve efficient and accurate risk assessment. The experimental results demonstrate that the proposed model significantly outperforms the traditional methods in key metrics, such as accuracy, recall, and F1 score, fully validating its practical application value in secondary operation scenarios within the power industry. Full article
(This article belongs to the Special Issue Diagnosis and Risk Analysis of Electrical Systems)
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21 pages, 4267 KiB  
Article
Development and Validation of a Low-Cost External Signal Acquisition Device for Smart Rail Pads: A Comparative Performance Study
by Amparo Guillén, Fernando Moreno-Navarro, Miguel Sol-Sánchez and Guillermo R. Iglesias
Sensors 2025, 25(6), 1933; https://doi.org/10.3390/s25061933 - 20 Mar 2025
Viewed by 201
Abstract
The development of cost-effective and reliable railway monitoring technologies is crucial for the maintenance of modern infrastructure. Embedding sensors into rail pads has emerged as a promising approach for monitoring wheel–track interactions, but the successful implementation of these systems requires a robust framework [...] Read more.
The development of cost-effective and reliable railway monitoring technologies is crucial for the maintenance of modern infrastructure. Embedding sensors into rail pads has emerged as a promising approach for monitoring wheel–track interactions, but the successful implementation of these systems requires a robust framework for signal data acquisition and analysis. This study validates a custom-designed External Signal Acquisition Device (ESAD) for use with smart rail pads, comparing its performance against a high-precision commercial analog module. While the commercial module delivers exceptional accuracy, its high cost, bulky size, and complex installation requirements limit its practicality for large-scale railway applications. Laboratory-scale and full-scale experiments simulating real-world railway conditions demonstrated that the custom ESAD performs comparably to the commercial module. During simulated train passages, the ESAD showed reduced signal dispersion as load and train speed increased, confirming its ability to provide reliable calibration data. Moreover, the device maintained over 95% reliability in analyzing load-to-signal linearity, ensuring consistent and dependable performance in both laboratory and field settings. However, the ESAD does have limitations, including slightly lower resolution for low frequencies and potential sensitivity to extreme environmental conditions, which may affect its performance in specific scenarios. These findings highlight the ESAD’s potential to strike a balance between cost and functionality, making it a viable solution for widespread railway monitoring applications. This research contributes to the advancement of affordable and efficient railway monitoring technologies, fostering the adoption of preventive maintenance practices and enhancing overall infrastructure performance. Full article
(This article belongs to the Special Issue Fault Detection, Diagnosis and Maintenance on Intelligent Transportation System)
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19 pages, 1656 KiB  
Article
Ultrasonic Time-of-Flight Diffraction Imaging Enhancement for Pipeline Girth Weld Testing via Time-Domain Sparse Deconvolution and Frequency-Domain Synthetic Aperture Focusing
by Eryong Wu, Ye Han, Bei Yu, Wei Zhou and Shaohua Tian
Sensors 2025, 25(6), 1932; https://doi.org/10.3390/s25061932 - 20 Mar 2025
Viewed by 162
Abstract
Ultrasonic TOFD imaging, as an important non-destructive testing method, has a wide range of applications in pipeline girth weld inspection and testing. Due to the limited bandwidth of ultrasonic transducers, near-surface defects in the weld are masked and cannot be recognized, resulting in [...] Read more.
Ultrasonic TOFD imaging, as an important non-destructive testing method, has a wide range of applications in pipeline girth weld inspection and testing. Due to the limited bandwidth of ultrasonic transducers, near-surface defects in the weld are masked and cannot be recognized, resulting in poor longitudinal resolution. Affected by the inherent diffraction effect of scattered acoustic waves, defect images have noticeable trailing, resulting in poor transverse resolution of TOFD imaging and making quantitative defect detection difficult. In this paper, based on the assumption of the sparseness of ultrasonic defect distribution, by constructing a convolutional model of the ultrasonic TOFD signal, the Orthogonal Matching Pursuit (OMP) sparse deconvolution algorithm is utilized to enhance the longitudinal resolution. Based on the synthetic aperture acoustic imaging model, in the wavenumber domain, backpropagation inference is implemented through phase transfer technology to eliminate the influence of diffraction effects and enhance transverse resolution. On this basis, the time-domain sparse deconvolution and frequency-domain synthetic aperture focusing methods mentioned above are combined to enhance the resolution of ultrasonic TOFD imaging. The simulation and experimental results indicate that this technique can outline the shape of defects with fine detail and improve image resolution by about 35%. Full article
(This article belongs to the Special Issue Ultrasound Imaging and Sensing for Nondestructive Testing)
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32 pages, 2784 KiB  
Article
Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
by Carolina Del-Valle-Soto, Leonardo J. Valdivia, Ramiro Velázquez, José A. Del-Puerto-Flores, José Varela-Aldás and Paolo Visconti
Sensors 2025, 25(6), 1931; https://doi.org/10.3390/s25061931 - 20 Mar 2025
Viewed by 280
Abstract
Wireless sensor networks (WSNs) are fundamental for modern IoT applications, yet they remain highly vulnerable to jamming attacks, which significantly degrade communication reliability and energy efficiency. This paper proposes a novel adaptive cluster-based jamming mitigation algorithm designed for heterogeneous WSNs that integrate LoRa [...] Read more.
Wireless sensor networks (WSNs) are fundamental for modern IoT applications, yet they remain highly vulnerable to jamming attacks, which significantly degrade communication reliability and energy efficiency. This paper proposes a novel adaptive cluster-based jamming mitigation algorithm designed for heterogeneous WSNs that integrate LoRa and Bluetooth Low Energy (BLE) technologies. The proposed strategy dynamically switches between communication protocols, optimizes energy consumption, and reduces retransmissions under interference conditions by leveraging real-time network topology adjustments and adaptive transmission power control. Through extensive experimental validation, we demonstrate that our mitigation mechanism reduces energy consumption by up to 38% and lowers packet retransmission rates by 47% compared to single-protocol networks under jamming conditions. Additionally, our results indicate that the hybrid LoRa-BLE approach outperforms standalone LoRa and BLE configurations in terms of network resilience, adaptability, and sustained data transmission under attack scenarios. This work advances the state-of-the-art by introducing a multi-protocol interference-resilient communication strategy, paving the way for more robust, energy-efficient, and secure WSN deployments in smart cities, industrial IoT, and critical infrastructure monitoring. Full article
(This article belongs to the Section Sensor Networks)
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19 pages, 5364 KiB  
Article
Enhancing Maritime Domain Awareness Through AI-Enabled Acoustic Buoys for Real-Time Detection and Tracking of Fast-Moving Vessels
by Jeremy Karst, Robert McGurrin, Kimberly Gavin, Joseph Luttrell, William Rippy, Robert Coniglione, Jason McKenna and Ralf Riedel
Sensors 2025, 25(6), 1930; https://doi.org/10.3390/s25061930 - 20 Mar 2025
Viewed by 469
Abstract
Acoustic target recognition has always played a central role in marine sensing. Traditional signal processing techniques that have been used for target recognition have shown limitations in accuracy, particularly with commodity hardware. To address such limitations, we present the results of our experiments [...] Read more.
Acoustic target recognition has always played a central role in marine sensing. Traditional signal processing techniques that have been used for target recognition have shown limitations in accuracy, particularly with commodity hardware. To address such limitations, we present the results of our experiments to assess the capabilities of AI-enabled acoustic buoys using OpenEar™, a commercial, off-the-shelf, software-defined hydrophone sensor, for detecting and tracking fast-moving vessels. We used a triangular sparse sensor network to investigate techniques necessary to estimate the detection, classification, localization, and tracking of boats transiting through the network. Emphasis was placed on evaluating the sensor’s operational detection range and feasibility of onboard AI for cloud-based data fusion. Results indicated effectiveness for enhancing maritime domain awareness and gaining insight into illegal, unreported, and unregulated activities. Additionally, this study provides a framework for scaling autonomous sensor networks to support persistent maritime surveillance. Full article
(This article belongs to the Special Issue Sensor Technologies for Ocean Environments: Impact Assessment, Monitoring and Protection)
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27 pages, 6368 KiB  
Article
Joint Entropy Error Bound of Two-Dimensional Direction-of-Arrival Estimation for L-Shaped Array
by Xiaolong Kong, Daxuan Zhao, Nan Wang and Dazhuan Xu
Sensors 2025, 25(6), 1929; https://doi.org/10.3390/s25061929 - 20 Mar 2025
Viewed by 163
Abstract
Several performance lower bounds have been studied for evaluating the accuracy of direction-of-arrival (DOA) estimation. However, lower bounds for joint estimation have not been fully explored when it comes to DOA estimation. The Cramér–Rao bound (CRB) can guarantee asymptotic tightness in the high-signal-to-noise [...] Read more.
Several performance lower bounds have been studied for evaluating the accuracy of direction-of-arrival (DOA) estimation. However, lower bounds for joint estimation have not been fully explored when it comes to DOA estimation. The Cramér–Rao bound (CRB) can guarantee asymptotic tightness in the high-signal-to-noise ratio (SNR) region but cannot provide tight performance bounds for parameter estimators in low- and medium-SNR regions. Consequently, we propose a tight performance bound for the joint estimation of azimuth and elevation DOAs in an L-shaped array. Firstly, the joint conditional probability density function (PDF) is given to establish the mathematical relationship among the receiving signal and the azimuth and elevation DOAs. Then, the joint a posteriori PDF is derived according to the Bayesian theorem. Next, the azimuth and elevation DOA entropy error bound (AEEEB) is derived as a global performance bound using the joint a posteriori entropy. Finally, the CRB and the mean square error (MSE) are provided for comparisons with the proposed performance bound. The simulation results indicate that the AEEEB provides a tighter performance bound compared to the CRB. Full article
(This article belongs to the Section Intelligent Sensors)
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25 pages, 6769 KiB  
Article
Performance Enhancement of Drone Acoustic Source Localization Through Distributed Microphone Arrays
by Jaejun Lim, Jaehan Joo and Suk Chan Kim
Sensors 2025, 25(6), 1928; https://doi.org/10.3390/s25061928 - 20 Mar 2025
Viewed by 220
Abstract
This paper presents a novel localization method that leverages two sets of distributed microphone arrays using the Generalized Cross-Correlation Phase Transform (GCC-PHAT) technique to improve the performance of anti-drone systems. In contrast to conventional sound source localization techniques, the proposed approach enhances localization [...] Read more.
This paper presents a novel localization method that leverages two sets of distributed microphone arrays using the Generalized Cross-Correlation Phase Transform (GCC-PHAT) technique to improve the performance of anti-drone systems. In contrast to conventional sound source localization techniques, the proposed approach enhances localization accuracy by precisely estimating the azimuth angle while considering the unique acoustic characteristics of drones. The effectiveness of the proposed method was validated through both simulations and field tests. Simulation results revealed that, in ideal channel conditions, the proposed method significantly reduced the mean and variance of localization errors compared to existing techniques, resulting in more accurate positioning. Furthermore, in noisy environments, the proposed approach consistently outperformed the comparison method across various Signal-to-Noise Ratio (SNR) levels, achieving up to 2.13 m of improvement at SNR levels above 0 dB. While the comparison method exhibited decreased localization accuracy along the y-axis and z-axis, the proposed method maintained stable performance across all axes by effectively distinguishing between azimuth and elevation angles. Field test results closely mirrored the simulation outcomes, further confirming the robustness and reliability of the proposed localization approach. Full article
(This article belongs to the Section Navigation and Positioning)
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28 pages, 13302 KiB  
Article
Feasibility of Oil Spill Detection in Port Environments Based on UV Imagery
by Marian-Daniel Iordache, Françoise Viallefont-Robinet, Gert Strackx, Lisa Landuyt, Robrecht Moelans, Dirk Nuyts, Joeri Vandeperre and Els Knaeps
Sensors 2025, 25(6), 1927; https://doi.org/10.3390/s25061927 - 20 Mar 2025
Viewed by 252
Abstract
Oil spills in ports are particular cases of oil pollution in water environments that call for specific monitoring measures. Apart from the ecological threats that they pose, their proximity to human activities and the financial losses induced by disturbed port activities add to [...] Read more.
Oil spills in ports are particular cases of oil pollution in water environments that call for specific monitoring measures. Apart from the ecological threats that they pose, their proximity to human activities and the financial losses induced by disturbed port activities add to the need for immediate action. However, in ports, established methods based on short-wave infrared sensors might not be applicable due to the relatively low thickness of the oil layer, and satellite images suffer from insufficient spatial resolution, given the agglomeration of objects in ports. In this study, a lightweight ultraviolet (UV) camera was exploited in both controlled experiments and a real port environment to estimate the potential and limitations of UV imagery in detecting oil spills, in comparison to RGB images. Specifically, motivated by the scarce research literature on this topic, we set up experiments simulating oil spills with various oil types, different viewing angles, and under different weather conditions, such that the separability between oil and background (water) could be better understood and objectively assessed. The UV camera was also used to detect real-world oil spills in a port environment after installing it on a vessel for continuous monitoring. Various separability metrics between water and oil, computed in both scenarios (controlled experiments and port environment), show that the UV cameras have better potential than RGB in detecting oil spills in port environments. Full article
(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)
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19 pages, 6428 KiB  
Article
New Method of Impact Localization on Plate-like Structures Using Deep Learning and Wavelet Transform
by Asaad Migot, Ahmed Saaudi and Victor Giurgiutiu
Sensors 2025, 25(6), 1926; https://doi.org/10.3390/s25061926 - 20 Mar 2025
Viewed by 207
Abstract
This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact [...] Read more.
This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact signals. These signals consisted of reflection waves that provided valuable information about impact events. In this methodology, each of the received signals was divided into several equal segments. Then, a wavelet transform (WT)-based time-frequency analysis was used for processing each segment signal. The generated WT diagrams of these segments’ signals were cropped and resized using MATLAB code to be used as input image datasets to train, validate, and test the proposed CNN model. Two scenarios were adopted from PAWS transducers. First, two sensors were positioned in two corners of the plate, while, in the second scenario, four sensors were used to monitor and collect the signals. Eight datasets were collected and reshaped from these two scenarios. These datasets presented the signals of two, three, four, and five impacts. The model’s performance was evaluated using four metrics: confusion matrix, accuracy, precision, and F1 score. The proposed model demonstrated exceptional performance by accurately localizing all of the impact points of the first scenario and 99% of the second scenario. The main limitation of the proposed model is how to differentiate the data samples that have similar features. From our point of view, the similarity challenge arose from two factors: the segmentation interval and the impact distance. First, applying the segmenting procedure to the PWAS signals led to an increase in the number of data samples. The procedure segmented each PWAS signal to 30 samples with equal intervals, regardless of the features of the signal. Segmenting and transforming different PWAS signals into image-based data points led to data samples that had similar features. Second, some of the impacts had a close distance to the PWAS sensors, which resulted in similar segmented signals. Therefore, the second scenario was more challenging for the proposed model. Full article
(This article belongs to the Special Issue Sensor and Sensing Technologies for Structural Health Monitoring and Non-Destructive Testing—2nd Edition)
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15 pages, 1599 KiB  
Article
Modern Water Treatment Technology Based on Industry 4.0
by David Guth and David Herák
Sensors 2025, 25(6), 1925; https://doi.org/10.3390/s25061925 - 20 Mar 2025
Viewed by 297
Abstract
Access to clean water remains a critical global challenge, particularly in under-resourced regions. This study introduces an autonomous water treatment system leveraging Industry 4.0 technologies, including advanced smart sensors, real-time monitoring, and automation. The system employs a multi-stage filtration process—mechanical, chemical, and UV [...] Read more.
Access to clean water remains a critical global challenge, particularly in under-resourced regions. This study introduces an autonomous water treatment system leveraging Industry 4.0 technologies, including advanced smart sensors, real-time monitoring, and automation. The system employs a multi-stage filtration process—mechanical, chemical, and UV sterilization—to treat water with varying contamination levels. Smart sensors play a pivotal role in ensuring precise control and adaptability across the entire process. Experimental validation was conducted on three water types: pond, river, and artificially contaminated water. Results revealed significant reductions in key contaminants such as PPM, pH, and electrical conductivity, achieving water quality standards set by the WHO. Statistical analyses confirmed the system’s reliability and adaptability under diverse conditions. These findings underscore the potential of smart, sensor-integrated, decentralized water treatment systems to effectively address global water security challenges. Future research could focus on scalability, renewable energy integration, and long-term operational durability to enhance applicability in remote areas. Full article
(This article belongs to the Section Industrial Sensors)
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23 pages, 6020 KiB  
Article
A Weighted-Transfer Domain-Adaptation Network Applied to Unmanned Aerial Vehicle Fault Diagnosis
by Jian Yang, Hairong Chu, Lihong Guo and Xinhong Ge
Sensors 2025, 25(6), 1924; https://doi.org/10.3390/s25061924 - 19 Mar 2025
Viewed by 189
Abstract
With the development of UAV technology, the composition of UAVs has become increasingly complex, interconnected, and tightly coupled. Fault features are characterized by weakness, nonlinearity, coupling, and uncertainty. A promising approach is the use of deep learning methods, which can effectively extract useful [...] Read more.
With the development of UAV technology, the composition of UAVs has become increasingly complex, interconnected, and tightly coupled. Fault features are characterized by weakness, nonlinearity, coupling, and uncertainty. A promising approach is the use of deep learning methods, which can effectively extract useful diagnostic information from weak, coupled, nonlinear data from inputs with background noise. However, due to the diversity of flight environments and missions, the distribution of the obtained sample data varies. The types of fault data and corresponding labels under different conditions are unknown, and it is time-consuming and expensive to label sample data. These challenges reduce the performance of traditional deep learning models in anomaly detection. To overcome these challenges, a novel weighted-transfer domain-adaptation network (WTDAN) method is introduced to realize the online anomaly detection and fault diagnosis of UAV electromagnetic-sensitive flight data. The method is based on unsupervised transfer learning, which can transfer the knowledge learnt from existing datasets to solve problems in the target domain. The method contains three novel multiscale modules: a feature extractor, used to extract multidimensional features from the input; a domain discriminator, used to improve the imbalance of the data distribution between the source domain and the target domain; and a label classifier, used to classify data categories for the target domain. Multilayer domain adaptation is used to reduce the distance between the source domain datasets and the target domain datasets distributions. The WTDAN assigns different weights to the source domain samples in order to weight the different contributions of source samples to solve the problem during the training process. The dataset adopts not only open datasets from the website but also test datasets from experiments to evaluate the transferability of the proposed WTDAN model. The experimental results show that, under the condition of fewer anomalous target data samples, the proposed method had a classification accuracy of up to 90%, which is higher than that of the other compared methods, and performed with superior transferability on the cross-domain datasets. The capability of fault diagnosis can provide a novel method for online anomaly detection and the prognostics and health management (PHM) of UAVs, which, in turn, would improve the reliability, repairability, and safety of UAV systems. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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13 pages, 3711 KiB  
Article
An Ultra-Wideband Handover System for GPS-Free Bridge Inspection Using Drones
by Ping-Hsiang Wang and Ruey-Beei Wu
Sensors 2025, 25(6), 1923; https://doi.org/10.3390/s25061923 - 19 Mar 2025
Viewed by 169
Abstract
This study proposes an ultra-wideband (UWB) handover system that increases the range of UWB positioning for bridge inspection using an unmanned aerial vehicle (UAV). A bipartite graph and a greedy algorithm are used, and the problem is transformed into vertex coloring to address [...] Read more.
This study proposes an ultra-wideband (UWB) handover system that increases the range of UWB positioning for bridge inspection using an unmanned aerial vehicle (UAV). A bipartite graph and a greedy algorithm are used, and the problem is transformed into vertex coloring to address the challenge of a large number of anchors and insufficient anchor IDs because the area is long and there are numerous beams and columns under the bridge. Simulation and experiment show that the solution reduces the number of anchors that are required from 27 to 14, which significantly saves deployment costs and reduces power consumption. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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